The point clouds acquired by a vehicle-borne mobile laser scanning (MLS) system have shown great potential for many applications such as intelligent transportation systems, road infrastructure inventories, and high-definition (HD) maps to support the advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). This paper presents a novel two-step approach to automated detection and reconstruction of three-dimensional (3D) highway curves from MLS point clouds. However, when dealing with noisy, unstructured, dense point clouds, we often face some challenges, most notably in handling of the outliers introduced during road marking detection and in recognition of curve types during 3D curve reconstruction. Our approach is formed by two main algorithms: a detector based on intensity variance and a robust model fitting estimator. The experimental results obtained using both a virtual scan dataset and a real MLS dataset demonstrated that our approach is very promising in handling of the outliers and reconstruction of 3D road curves. Specifically, a relative accuracy of 0.6% has been achieved in estimation of circle radii based on the virtual scan dataset. A comparative study also showed that our road marking detection approach is more effective and more stable than state-of-the-art approaches.
3D Highway Curve Reconstruction From Mobile Laser Scanning Point Clouds
IEEE Transactions on Intelligent Transportation Systems ; 21 , 11 ; 4762-4772
2020-11-01
3865262 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Traffic Sign Occlusion Detection Using Mobile Laser Scanning Point Clouds
Online Contents | 2017
|Traffic Sign Occlusion Detection Using Mobile Laser Scanning Point Clouds
Online Contents | 2017
|Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds
Transportation Research Record | 2022
|Highway scene reconstruction system and method based on road point clouds
Europäisches Patentamt | 2021
|